{
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"
    }
   },
   "cell_type": "markdown",
   "id": "de28f87c",
   "metadata": {},
   "source": [
    "# Overview\n",
    "某公司对自身购买按钮进行UI改版，将底色变为红色，实验组和对照组方案如下所示：\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed70790a",
   "metadata": {},
   "source": [
    "产品从2020年5月7日到2020年6月25日进行实验，请对实验数据进行分析，判断该次改版是否成功"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7227ab15",
   "metadata": {},
   "source": [
    "# Analysis 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7187877c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>对照组</th>\n",
       "      <th>实验组</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020/5/27</td>\n",
       "      <td>0.586098</td>\n",
       "      <td>0.591154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020/5/28</td>\n",
       "      <td>0.590029</td>\n",
       "      <td>0.598811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020/5/29</td>\n",
       "      <td>0.594595</td>\n",
       "      <td>0.596829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020/5/30</td>\n",
       "      <td>0.594600</td>\n",
       "      <td>0.606838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/5/31</td>\n",
       "      <td>0.610615</td>\n",
       "      <td>0.593986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期       对照组       实验组\n",
       "0  2020/5/27  0.586098  0.591154\n",
       "1  2020/5/28  0.590029  0.598811\n",
       "2  2020/5/29  0.594595  0.596829\n",
       "3  2020/5/30  0.594600  0.606838\n",
       "4  2020/5/31  0.610615  0.593986"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt \n",
    "import os \n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "csv_path1 = \"/Users/collinsliu/learning/jobs/self advance/course/week4/实验案例-1.csv\"\n",
    "test1_pd = pd.read_csv(csv_path1,encoding='utf-8')\n",
    "test1_pd.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8e9965a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>对照组</th>\n",
       "      <th>实验组</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.600010</td>\n",
       "      <td>0.599209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.015898</td>\n",
       "      <td>0.012711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.563410</td>\n",
       "      <td>0.577538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.590292</td>\n",
       "      <td>0.591438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.598522</td>\n",
       "      <td>0.597820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.612165</td>\n",
       "      <td>0.606735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.623325</td>\n",
       "      <td>0.624049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             对照组        实验组\n",
       "count  30.000000  30.000000\n",
       "mean    0.600010   0.599209\n",
       "std     0.015898   0.012711\n",
       "min     0.563410   0.577538\n",
       "25%     0.590292   0.591438\n",
       "50%     0.598522   0.597820\n",
       "75%     0.612165   0.606735\n",
       "max     0.623325   0.624049"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test1_pd[\"日期\"] = pd.to_datetime(test1_pd[\"日期\"])\n",
    "test1_pd.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61dd0af",
   "metadata": {},
   "source": [
    "由以上分析我们可以看出，两组对照实验的方差是不相同的，<br>并且二者的样本数量都在30，因此我们采取独立方差的t检验进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5c0792",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "$$\n",
    "    H_0 = 实验组\\mu_{0}和对照组\\mu_{1}没有明显差异 \\\\\n",
    "    H_1 = 实验组\\mu_{0}和对照组\\mu_{1}具有明显差异\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8bff9252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=0.21571325432487554, pvalue=0.8300046384759966)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "ttest_ind(test1_pd[\"对照组\"],test1_pd[\"实验组\"],equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8f4817",
   "metadata": {},
   "source": [
    "## Result\n",
    "由于p远远大于显著性参数$\\alpha=0.05$，因此我们有$95\\%$的信心认为实验组和对照组之间是没有差异的"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d96afe91",
   "metadata": {},
   "source": [
    "# Overview\n",
    "数据分析师和产品经理对一期实验数据进行分析后，修正了方案中不合理的点，重新进行实验<br>请对实验结果进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffcfa538",
   "metadata": {},
   "source": [
    "# Analysis 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e7a9d6",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "$$\n",
    "    H_0 = 实验组\\mu_{0}和对照组\\mu_{1}没有明显差异 \\\\\n",
    "    H_1 = 实验组\\mu_{0}和对照组\\mu_{1}具有明显差异\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99a1fd40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>对照组</th>\n",
       "      <th>实验组</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020/7/17</td>\n",
       "      <td>0.658365</td>\n",
       "      <td>0.427530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020/7/18</td>\n",
       "      <td>0.664070</td>\n",
       "      <td>0.706173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020/7/19</td>\n",
       "      <td>0.650354</td>\n",
       "      <td>0.668994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020/7/20</td>\n",
       "      <td>0.643771</td>\n",
       "      <td>0.832125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020/7/21</td>\n",
       "      <td>0.635349</td>\n",
       "      <td>0.828405</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期       对照组       实验组\n",
       "0  2020/7/17  0.658365  0.427530\n",
       "1  2020/7/18  0.664070  0.706173\n",
       "2  2020/7/19  0.650354  0.668994\n",
       "3  2020/7/20  0.643771  0.832125\n",
       "4  2020/7/21  0.635349  0.828405"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_path2 = \"/Users/collinsliu/learning/jobs/self advance/course/week4/实验案例-2.csv\"\n",
    "test2_df = pd.read_csv(csv_path2,encoding='utf-8')\n",
    "test2_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96869b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>对照组</th>\n",
       "      <th>实验组</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.647290</td>\n",
       "      <td>0.779995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.012044</td>\n",
       "      <td>0.090416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.622657</td>\n",
       "      <td>0.427530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.638597</td>\n",
       "      <td>0.743284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.648101</td>\n",
       "      <td>0.804069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.654980</td>\n",
       "      <td>0.835143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.672840</td>\n",
       "      <td>0.912588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             对照组        实验组\n",
       "count  49.000000  49.000000\n",
       "mean    0.647290   0.779995\n",
       "std     0.012044   0.090416\n",
       "min     0.622657   0.427530\n",
       "25%     0.638597   0.743284\n",
       "50%     0.648101   0.804069\n",
       "75%     0.654980   0.835143\n",
       "max     0.672840   0.912588"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2_df[\"日期\"] = pd.to_datetime(test2_df[\"日期\"])\n",
    "test2_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b3822f",
   "metadata": {},
   "source": [
    "同理，由以上分析我们可以看出，两组对照实验的方差是不相同的，<br>并且二者的样本数量都在30，因此我们采取独立方差的t检验进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a8dd89d",
   "metadata": {},
   "source": [
    "## Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58eddc52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-10.18412542802433, pvalue=9.287704517004857e-14)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttest_ind(test2_df[\"对照组\"],test2_df[\"实验组\"],equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bebc95d",
   "metadata": {},
   "source": [
    "由于p远远小于显著性参数$\\alpha=0.05$，因此我们有$95\\%$的信心拒绝原假设，<br>也就是说认为实验组和对照组之间具有显著差异，实验是成功的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a66ee67",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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